IEEE

Tree Variational Autoencoder for Code

Abstract: Autoencoder models of source code are an emerging alternative to autoregressive large language models with important benefits for genetic improvement of software. We hypothesize that encoder ...
GitHub

Variational Autoencoder (VAE) for Image Classification

This project focuses on learning useful representations from unlabelled data for downstream tasks, specifically categorizing images into one of N categories using Variational Autoencoders (VAE). The ...
IEEE

Automatically Inverse Design of FSSs by Conditional Variational Autoencoder and Equivalent Circuit Models With Large Language Models

Abstract: Recently, deep learning has attracted intensive attentions in electromagnetic society, especially for inverse problems. In this work, we propose two language-guided automatic design ...
GitHub

ti2ni1m/Image-Generation-with-Conditional-Variational-Autoencoder-C-VAE-and-Conditional-GAN-C-GAN-

In this project, I aim to create a set of images of Kuzushiji Japanese characters via the Kuzushiji-49 dataset using 2 graphical models which are Conditional-Variational Autoencoder(C-VAE), and ...
Frontiers

Accurate identification of snoRNA targets using variational graph autoencoder to advance the redevelopment of traditional medicines

Existing studies indicate that dysregulation or abnormal expression of small nucleolar RNA (snoRNA) is closely associated with various diseases, including lung cancer. Furthermore, these diseases ...
C&EN

Soft Measurement Modeling Method Based on Quality-Related Mixed Variational Autoencoder Regression

Huzhou Key Laboratory of Intelligent Sensing and Optimal Control for Industrial Systems, School of Engineering, Huzhou University, Huzhou 313000, PR China Zhejiang Key Laboratory of Industrial Solid ...